AIMC Topic: Phenotype

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Multiple, Single Trait GWAS and Supervised Machine Learning Reveal the Genetic Architecture of Fraxinus excelsior Tolerance to Ash Dieback in Europe.

Plant, cell & environment
Common ash (Fraxinus excelsior) is under intensive attack from the invasive alien pathogenic fungus Hymenoscyphus fraxineus, causing ash dieback at epidemic levels throughout Europe. Previous studies have found significant genetic variation among gen...

Single-cell microbiota phenotyping reveals distinct disease and therapy-associated signatures in Crohn's disease.

Gut microbes
IgA-coated fractions of the intestinal microbiota of Crohn's disease (CD) patients have been shown to contain taxa that hallmark the compositional dysbiosis in CD microbiomes. However, the correlation between other cellular properties of intestinal b...

Deep learning-based assessment of missense variants in the gene presented with bilateral congenital cataract.

BMJ open ophthalmology
OBJECTIVE: We compared the protein structure and pathogenicity of clinically relevant variants of the gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time.

Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer.

Breast cancer research : BCR
Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a m...

A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification.

Scientific reports
The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling f...

Applying artificial intelligence to uncover the genetic landscape of coagulation factors.

Journal of thrombosis and haemostasis : JTH
Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines ...

Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.

Veterinary microbiology
Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD); however, antibiotic resistance in this species is increasing, making treatment more difficult. Integrative-conjugative elements (ICE), a subset of ...

Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.

Journal of translational medicine
INTRODUCTION: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment de...

Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data.

Nature communications
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentati...

Rapid and accurate multi-phenotype imputation for millions of individuals.

Nature communications
Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been ...